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Go Machine Learning Projects

  • Course Code: Data Science - Go Machine Learning Projects
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to Work through exciting projects to explore the capabilities of Go and Machine Learning

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to Work through exciting projects to explore the capabilities of Go and Machine Learning 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This course will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured. The course begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you’ll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project. By the end of this course, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects. 

Working in a hands-on learning environment, led by our Go Machine Learning expert instructor, students will learn about and explore: 

  • Explore ML tasks and Go’s machine learning ecosystem 
  • Implement clustering, regression, classification, and neural networks with Go 
  • Get to grips with libraries such as Gorgonia, Gonum, and GoCv for training models in Go 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Set up a machine learning environment with Go libraries 
  • Use Gonum to perform regression and classification 
  • Explore time series models and decompose trends with Go libraries 
  • Clean up your Twitter timeline by clustering tweets 
  • Learn to use external services for your machine learning needs 
  • Recognize handwriting using neural networks and CNN with Gorgonia 
  • Implement facial recognition using GoCV and OpenCV 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Work through exciting projects to explore the capabilities of Go and Machine Learning 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. How to Solve All Machine Learning Problems 
  • How to Solve All Machine Learning Problems 
  • What is a problem?  
  • What is an algorithm?  
  • What is machine learning?  
  • Do you need machine learning? 
  • The general problem solving process 
  • What is a model? 
  • On writing and lesson organization  
  • Why Go?  
  • Quick start 
  • Functions 
  • Variables 
  1. Linear Regression – House Price Prediction 
  • Linear Regression – House Price Prediction 
  • The project 
  • Exploratory data analysis 
  • Linear regression 
  • Discussion and further work 
  1. Classification – Spam Email Detection 
  • Classification – Spam Email Detection 
  • The project  
  • Exploratory data analysis  
  • The classifier 
  • Naive Bayes 
  • Implementating the classifier 
  • Putting it all together 
  1. Decomposing CO2 Trends Using Time Series Analysis 
  • Decomposing CO2 Trends Using Time Series Analysis 
  • Exploratory data analysis 
  • Decomposition 
  • Forecasting 
  1. Clean Up Your Personal Twitter Timeline by Clustering Tweets 
  • Clean Up Your Personal Twitter Timeline by Clustering Tweets 
  • The project  
  • K-means  
  • DBSCAN 
  • Data acquisition 
  • Exploratory data analysis 
  • Data massage 
  • Clustering  
  • Real data 
  • The program  
  • Tweaking the program 
  1. Neural Networks – MNIST Handwriting Recognition 
  • Neural Networks – MNIST Handwriting Recognition 
  • A neural network 
  • Linear algebra 101 
  • Learning 
  • The project 
  • Training the neural network 
  • Cross-validation 
  1. Convolutional Neural Networks – MNIST Handwriting Recognition 
  • Convolutional Neural Networks – MNIST Handwriting Recognition 
  • Everything you know about neurons is wrong  
  • Neural networks – a redux 
  • The project 
  • CNNs 
  • Describing a CNN 
  • Running the neural network 
  • Testing 
  1. Basic Facial Detection 
  • Basic Facial Detection 
  • What is a face?  
  • PICO  
  • GoCV  
  • Pigo 
  • Face detection program  
  • Evaluating algorithms 
  1. Hot Dog or Not Hot Dog – Using External Services 
  • Hot Dog or Not Hot Dog – Using External Services 
  • MachineBox 
  • What is MachineBox? 
  • The project 
  • The results 
  • What does this all mean?  
  • Why MachineBox? 
  1. What’s Next? 
  • What’s Next? 
  • What should the reader focus on?  
  • The researcher, the practitioner, and their stakeholder 
  • What did this course not cover? 
  • Where can I learn more? 
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